Data-driven Deep Learning (DL) models have been a promising solution to overcome the aforementioned issues like array-dependent parameters or coherent interference due to their powerful ability to learn high level features from high-dimensional structured data at multi-scale resolutions.
As two examples of early supervised learning techniques applied to DoA estimation, [1]} uses SVM to estimate DoA even when the manifold is unknown, and [2]} was the first to propose a one layer neural network with radial-basis functions to estimate DoA.
More recently, Convolution Neural Networks (CNN) were first adopted in [3]} for multiple speaker DoA estimation.
It was shown that a CNN can learn the representational features of spectral images obtained via the short-time Fourier transform (STFT), and can provide performance better than MUSIC.
Unfortunately, the computational complexity required to generate the spectral images limit the model's real-time applicability.
On the other hand, [4]} [5]} [6]} use the covariance matrix as the input feature with uncorrelated signals, since it is more compact and easy to generate from the received signal.
One issue shared by all of these techniques is the required knowledge of the MOE.
